utils.py 4.86 KB
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import os
import time
import hashlib
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import getpass
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import datetime
import torch
import torch.nn as nn
import logging
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm
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from sklearn.metrics import roc_auc_score, precision_recall_curve, f1_score, auc
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plt.rcParams["figure.dpi"] = 288 # increase dpi for clearer plots
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# PARAMS =======================
INPUT_SIZE = (96, 256)
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MAX_LENGTH = 10000
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# CONFIG =======================

# paths:
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PATH_PROJECT_ROOT = os.path.dirname(os.path.realpath(__file__))
PATH_RESULTS = os.path.join(PATH_PROJECT_ROOT, 'results')

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hostname = os.uname()[1]
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username = getpass.getuser()

if hostname in ['rechenknecht3.cp.jku.at', 'rechenknecht2.cp.jku.at']:
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    plt.switch_backend('agg')
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    PATH_DATA_ROOT = '/media/rk3/shared/datasets/MTG-Jamendo'
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    USE_GPU = True
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elif hostname == 'hermine':  # PC verena
    plt.switch_backend('agg')
    PATH_DATA_ROOT = '/media/verena/SAMSUNG/Data/MTG-Jamendo'
    USE_GPU = True
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elif hostname == 'verena-830g5': # Laptop Verena
    PATH_DATA_ROOT = '/media/verena/SAMSUNG/Data/MTG-Jamendo'
    USE_GPU = False
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elif hostname == 'shreyan-HP': # Laptop Shreyan
    PATH_DATA_ROOT = '/home/shreyan/mounts/home@rk3/shared/datasets/MTG-Jamendo'
    USE_GPU = False
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else:
    PATH_DATA_ROOT = '/mnt/2tb/datasets/MTG-Jamendo'
    USE_GPU = False

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if username == 'verena':
    PATH_RESULTS = '/home/verena/experiments/moodwalk/'


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PATH_AUDIO = os.path.join(PATH_DATA_ROOT, 'MTG-Jamendo_audio')
PATH_ANNOTATIONS = os.path.join(PATH_DATA_ROOT, 'MTG-Jamendo_annotations')
PATH_MELSPEC_DOWNLOADED = os.path.join(PATH_DATA_ROOT, 'MTG-Jamendo_melspec_downloaded')
PATH_MELSPEC_DOWNLOADED_FRAMED = os.path.join(PATH_MELSPEC_DOWNLOADED, 'framed')
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PATH_MELSPEC_DOWNLOADED_HDF5 = os.path.join(PATH_DATA_ROOT, 'HDF5Cache_spectrograms')
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TRAINED_MODELS_PATH = ''

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# run name
def make_run_name(suffix=''):
    assert ' ' not in suffix
    hash = hashlib.sha1()
    hash.update(str(time.time()).encode('utf-8'))
    run_hash = hash.hexdigest()[:5]
    name = run_hash + suffix
    return name

curr_run_name = make_run_name()
CURR_RUN_PATH = os.path.join(PATH_RESULTS, 'runs', curr_run_name)

if not os.path.isdir(CURR_RUN_PATH):
    os.mkdir(CURR_RUN_PATH)

# SET UP LOGGING =============================================
filelog = logging.getLogger()
streamlog = logging.getLogger()
logger = logging.getLogger()
fh = logging.FileHandler(os.path.join(CURR_RUN_PATH, f'{curr_run_name}.log'))
sh = logging.StreamHandler()
formatter = logging.Formatter('%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s')
fh.setFormatter(formatter)
sh.setFormatter(formatter)

# filelog logs only to file
filelog.addHandler(fh)
filelog.setLevel(logging.INFO)

# streamlog logs only to terminal
streamlog.addHandler(sh)
streamlog.setLevel(logging.INFO)

# logger logs to both file and terminal
logger.addHandler(fh)
logger.addHandler(sh)
logger.setLevel(logging.DEBUG)

# ============================================

def write_to_file(data, path):
    # not fully implemented. unused function as of now.
    with open(path, 'w') as f:
        if isinstance(data, np.ndarray):
            for i in data:
                f.writelines(i)


def dims_calc(obj, in_shape):
    """
    utility function to calculate output dimensions of a conv2d or maxpool2d stage
    """
    kernel_size = obj.kernel_size
    stride = obj.stride
    padding = obj.padding
    dilation = obj.dilation
    h_in = in_shape[0]
    w_in = in_shape[1]

    if isinstance(obj, nn.Conv2d):
        h_out = int(((h_in + 2*padding[0] - dilation[0]*(kernel_size[0]-1))/stride[0])+1)
        w_out = int(((w_in + 2*padding[1] - dilation[1]*(kernel_size[1]-1))/stride[1])+1)
        out_shape = [h_out, w_out, obj.out_channels]
    elif isinstance(obj, nn.MaxPool2d):
        if isinstance(padding, int):
            padding = (padding, padding)
        if isinstance(dilation, int):
            dilation = (dilation, dilation)
        if isinstance(kernel_size, int):
            kernel_size = (kernel_size, kernel_size)
        if isinstance(stride, int):
            stride = (stride, stride)

        h_out = int(((h_in + 2 * padding[0] - dilation[0] * (kernel_size[0] - 1) -1) / stride[0]) + 1)
        w_out = int(((w_in + 2 * padding[1] - dilation[1] * (kernel_size[1] - 1)-1) / stride[1]) + 1)
        out_shape = [h_out, w_out, in_shape[2]]
    else:
        out_shape = [None, None, None]
    return out_shape



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def save(model, path):
    try:
        torch.save(model.module.state_dict(), path)
    except AttributeError:
        torch.save(model.state_dict(), path)


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if __name__=='__main__':
    # TESTS

    # c = nn.Conv2d(512, 256, 1, 1, 0) # (in_channels, out_channels, kernel_size, stride, padding)
    # m = nn.MaxPool2d(2)
    # print(dims_calc(c, [37, 17, 512]))
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    # preprocess_specs(source_root=PATH_MELSPEC_DOWNLOADED,
    #                  destination_root=PATH_MELSPEC_DOWNLOADED_FRAMED)
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    pass